{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "import torch\n",
    "import torchvision\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "traindata_0_path = r'../../data/motor_fault/Motor_tain/Negative/'\n",
    "traindata_1_path = r'../../data/motor_fault/Motor_tain/Positive//'\n",
    "testdata_path = r'../../data/motor_fault/Motor_testP/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**电机正反转处理：分别用正转和反转数据训练两个模型，预测也分别预测，最后只要有一个模型判断样本为故障，则认为该电机故障**  \n",
    "**下面0代表负例或正常电机，1代表正例或故障电机**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Farward test data number:  5738\n"
     ]
    }
   ],
   "source": [
    "# 读取正转测试数据\n",
    "testdata_F = []\n",
    "motor_name_F = []\n",
    "for i in os.listdir(testdata_path):\n",
    "    if 'F' in i:\n",
    "        single_testdata_F_path = os.path.join(testdata_path, i)\n",
    "        testdata_F.append(pd.read_csv(single_testdata_F_path))\n",
    "        motor_name_F.append(i)\n",
    "num_test_F = len(testdata_F)\n",
    "print(\"Farward test data number: \", num_test_F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backward test data number:  5738\n"
     ]
    }
   ],
   "source": [
    "# 读取反转测试数据\n",
    "testdata_B = []\n",
    "motor_name_B = []\n",
    "for i in os.listdir(testdata_path):\n",
    "    if 'B' in i:\n",
    "        single_testdata_B_path = os.path.join(testdata_path, i)\n",
    "        testdata_B.append(pd.read_csv(single_testdata_B_path))\n",
    "        motor_name_B.append(i)\n",
    "num_test_B = len(testdata_B)\n",
    "print(\"Backward test data number: \", num_test_B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据查看和探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testdata_F[0].head() \n",
      "         ai1       ai2\n",
      "0 -0.013206 -0.019872\n",
      "1 -0.014118 -0.019326\n",
      "2 -0.016878 -0.018836\n",
      "3 -0.019307 -0.020391\n",
      "4 -0.019872 -0.023095\n",
      "testdata_F[0].shape:  (79999, 2)\n"
     ]
    }
   ],
   "source": [
    "print(\"testdata_F[0].head()\", \"\\n\", testdata_F[0].head())\n",
    "print(\"testdata_F[0].shape: \", testdata_F[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testdata_B[0].head() \n",
      "         ai1       ai2\n",
      "0 -0.018009  0.000714\n",
      "1 -0.018898  0.000886\n",
      "2 -0.018154  0.000661\n",
      "3 -0.017624  0.000843\n",
      "4 -0.018317 -0.000894\n",
      "testdata_B[0].shape:  (79999, 2)\n"
     ]
    }
   ],
   "source": [
    "print(\"testdata_B[0].head()\",\"\\n\", testdata_B[0].head())\n",
    "print(\"testdata_B[0].shape: \",testdata_B[0].shape)"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征提取\n",
    "思路：对每一个电机的数据，用滑动窗口法提取特征，提取的特征如下：\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_extraction(series_data, window_width=10000, steps=100):\n",
    "    # series_data is input\n",
    "    # series_data = np.array(traindata_0_F[0].iloc[:,0])\n",
    "\n",
    "    # sliding window\n",
    "    window_width = 10000\n",
    "    t_start = 0\n",
    "    windows = []\n",
    "   \n",
    "    while t_start+window_width <= len(series_data):\n",
    "        windows.append(series_data[t_start:t_start+window_width])\n",
    "        t_start += steps\n",
    "    windows_num = len(windows)\n",
    "    windows = np.array(windows)\n",
    "    #print(\"windows.shape: {},t_start: {}\".format(windows.shape, t_start))\n",
    "\n",
    "    f1 = []\n",
    "    f2 = []\n",
    "    f3 = []\n",
    "    f4 = []\n",
    "    f5 = []\n",
    "    f6 = []\n",
    "    f7 = []\n",
    "    f8 = []\n",
    "    f9 = []\n",
    "    f10 = []\n",
    "    f11 = []\n",
    "    for i in range(windows_num):\n",
    "        temp = windows[i]\n",
    "        data_max = np.max(temp)\n",
    "        data_min = np.min(temp)\n",
    "        # 时域特征\n",
    "        f1_temp = data_max - data_min\n",
    "        f1.append(f1_temp)\n",
    "        f2_temp = np.sqrt(np.mean(temp ** 2))\n",
    "        f2.append(f2_temp)\n",
    "        f3_temp = f2_temp / abs(np.mean(temp))\n",
    "        f3.append(f3_temp)\n",
    "        f4_temp = data_max / abs(np.mean(temp))\n",
    "        f4.append(f4_temp)\n",
    "        f5_temp = np.mean((abs(temp) - np.mean(temp)) ** 4) / (f2_temp ** 4)\n",
    "        f5.append(f5_temp)\n",
    "        f6_temp = data_max / f2_temp\n",
    "        f6.append(f6_temp)\n",
    "        f7_temp = f2_temp / np.mean(temp)\n",
    "        f7.append(f7_temp)\n",
    "        # 频域特征\n",
    "        '''\n",
    "        f8_temp = np.sum(temp * temp) / (2 * np.pi * np.sum(temp ** 2)) #分子不对\n",
    "        f8.append(f8_temp)\n",
    "        f9_temp = np.sum(temp ** 2) / (4 * (np.pi ** 2) * np.sum(temp ** 2)) #分子不对\n",
    "        f9.append(f9_temp)\n",
    "        f10_temp = \n",
    "        f10.append(f10_temp)\n",
    "        f11_temp = 0\n",
    "        f11.append(f11_temp)\n",
    "        '''\n",
    "        \n",
    "    f1 = np.array(f1)\n",
    "    f2 = np.array(f2)\n",
    "    f3 = np.array(f3)\n",
    "    f4 = np.array(f4)\n",
    "    f5 = np.array(f5)\n",
    "    f6 = np.array(f6)\n",
    "    f7 = np.array(f7)\n",
    "    f8 = np.array(f8)\n",
    "    f9 = np.array(f9)\n",
    "    f10 = np.array(f10)\n",
    "    f11 = np.array(f11)\n",
    "\n",
    "    features = np.concatenate((f1[:, np.newaxis], f2[:, np.newaxis], f3[:, np.newaxis],\n",
    "                               f4[:, np.newaxis], f5[:, np.newaxis], f6[:, np.newaxis], \n",
    "                               f7[:, np.newaxis]), axis=1)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**目前频域特征还有问题，只用前7个时域特征即可**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5738"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(testdata_F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test_F.shape:  (5738, 1400, 14)\n",
      "X_test_F.shape:  (8033200, 14)\n"
     ]
    }
   ],
   "source": [
    "# 正转的测试数据testdata_F\n",
    "X_test_F = []\n",
    "X_test_F_motor_name = []\n",
    "for i in range(len(testdata_F)):\n",
    "    features1 = feature_extraction(np.array(testdata_F[i].iloc[:,0]), window_width=10000, steps=50)\n",
    "    features2 = feature_extraction(np.array(testdata_F[i].iloc[:,1]), window_width=10000, steps=50)\n",
    "    features = np.concatenate((np.array(features1), np.array(features2)),axis=1)\n",
    "    X_test_F.append(features)\n",
    "    for j in range(features.shape[0]):\n",
    "        X_test_F_motor_name.append(motor_name_F[i])\n",
    "X_test_F = np.array(X_test_F)\n",
    "print(\"X_test_F.shape: \", X_test_F.shape)\n",
    "X_test_F = X_test_F.reshape((-1, 14))\n",
    "print(\"X_test_F.shape: \", X_test_F.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存数据，其中X_test_F.csv里面是测试数据的X，motor_name_test_F.csv是测试数据对应行的电机名\n",
    "#X_test_F = np.concatenate((X_test_F, label), axis=1)\n",
    "print(\"X_test_F.shape: \", X_test_F.shape)\n",
    "print(\"len(X_test_F_motor_name): \",len(X_test_F_motor_name))\n",
    "np.savetxt(\"../../data/motor_fault/X_test_F.csv\", X_test_F, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8033200,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(X_test_F_motor_name).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.savetxt(\"../../data/motor_fault/X_test_F_motor_name.csv\", np.array(X_test_F_motor_name), delimiter=\",\")  #不是数字，不能用此函数写到文件里\n",
    "f = open(\"../../data/motor_fault/X_test_F_motor_name.csv\",\"w\")\n",
    "f.write(str(X_test_F_motor_name))\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test_B.shape (5738, 1400, 14)\n",
      "X_test_B.shape (8033200, 14)\n"
     ]
    }
   ],
   "source": [
    "# 反转的测试数据testdata_B\n",
    "X_test_B = []\n",
    "X_test_B_motor_name = []\n",
    "for i in range(len(testdata_B)):\n",
    "    features1 = feature_extraction(np.array(testdata_B[i].iloc[:,0]), window_width=10000, steps=50)\n",
    "    features2 = feature_extraction(np.array(testdata_B[i].iloc[:,1]), window_width=10000, steps=50)\n",
    "    features = np.concatenate((np.array(features1), np.array(features2)),axis=1)\n",
    "    X_test_B.append(features)\n",
    "    for j in range(features.shape[0]):\n",
    "        X_test_B_motor_name.append(motor_name_B[i])\n",
    "X_test_B = np.array(X_test_B)\n",
    "print(\"X_test_B.shape\", X_test_B.shape)\n",
    "X_test_B = X_test_B.reshape((-1, 14))\n",
    "print(\"X_test_B.shape\", X_test_B.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test_B.shape:  (8033200, 14)\n",
      "len(X_test_B_motor_name):  8033200\n"
     ]
    }
   ],
   "source": [
    "# 保存数据，其中X_test_B.csv里面是测试数据的X，motor_name_test_B.csv是测试数据对应行的电机名\n",
    "#X_test_B = np.concatenate((X_test_B, label), axis=1)\n",
    "print(\"X_test_B.shape: \", X_test_B.shape)\n",
    "print(\"len(X_test_B_motor_name): \",len(X_test_B_motor_name))\n",
    "np.savetxt(\"../../data/motor_fault/X_test_B.csv\", X_test_B, delimiter=\",\")\n",
    "#np.savetxt(\"../../data/motor_fault/motor_name_test_B.csv\", motor_name_test_B, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"../../data/motor_fault/X_test_B_motor_name.csv\",\"w\")\n",
    "f.write(str(X_test_B_motor_name))\n",
    "f.close()"
   ]
  }
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